Bimodal learning via trilogy of skip-connection deep networks for diabetic retinopathy risk progression identification.

Journal: International journal of medical informatics
Published Date: Aug 5, 2019

Abstract

BACKGROUND: Diabetic Retinopathy (DR) is considered a pathology of retinal vascular complications, which stays in the top causes of vision impairment and blindness. Therefore, precisely inspecting its progression enables the ophthalmologists to set up appropriate next-visit schedule and cost-effective treatment plans. In the literature, existing work only makes use of numerical attributes in Electronic Medical Records (EMR) for acquiring such kind of DR-oriented knowledge through conventional machine learning techniques, which require an exhaustive job of engineering most impactful risk factors.

Authors

  • Cam-Hao Hua
    Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea.
  • Thien Huynh-The
  • Kiyoung Kim
    Department of Ophthalmology, Kyung Hee University Medical Center, Kyung Hee University, Seoul 02447, South Korea. Electronic address: pourma@naver.com.
  • Seung-Young Yu
    Department of Ophthalmology, Kyung Hee University Medical Center, Kyung Hee University, Seoul 02447, South Korea. Electronic address: syyu@khu.ac.kr.
  • Thuong Le-Tien
    Department of Electrical and Electronics Engineering, Ho Chi Minh City University of Technology, Ho Chi Minh City, 700000, Vietnam. Electronic address: thuongle@hcmut.edu.vn.
  • Gwang Hoon Park
    Department of Computer Science and Engineering, Kyung Hee University, Gyeonggi-do 17104, South Korea. Electronic address: ghpark@khu.ac.kr.
  • Jaehun Bang
    Department of Computer Science and Engineering, College of Software, Kyung Hee University, 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 17104 Republic of Korea.
  • Wajahat Ali Khan
    Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu Yongin-si, Gyeonggi-do 446-701, Korea. wajahat.alikhan@oslab.khu.ac.kr.
  • Sung-Ho Bae
    Department of Computer Science and Engineering, Kyung Hee University, Gyeonggi-do 17104, South Korea. Electronic address: shbae@khu.ac.kr.
  • Sungyoung Lee
    Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea.

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